Case Study

Balancing Petroleum Pipeline Field Service with Transportation Optimization

Balancing Pipeline Field Service Assignments and Routes Using Transportation Network Modeling

Challenge

A growing energy manufacturing and logistics company needed to better manage the growing imbalance in workload among their Field Service Representatives (FSRs) that serviced the company’s 15,000 miles of pipeline. The company wanted to use modeling technology to balance the workload among all FSRs, assign pipeline locations to FSRs, how to manage standard hourly rates versus overtime hourly costs, identify the best route to service each oil pipeline location and understand how the system would need to be adjusted as new pipeline locations were added in the future.

Solution

The LLamasoft team used Transportation Guru to develop an end-to-end model of the existing field service structure and the optimized structure, and created repeatable process to use for future planning.

Transportation Guru was used to geocode all of the FSR locations and oil pipeline servicing locations throughout North America. Greenfield analysis was used to determine optimal locations to hire new FSRs throughout North America. Network optimization was then run to determine which pipeline locations should be assigned to which FSRs. In order to determine this, the shipment schedule was set up in number of service hours required at each pipeline location and how frequently the pipeline requires service. Some locations required monthly visits, while other locations required a visit every six months. The solution balanced the pipeline location assignments among the FSRs while taking into account the number of location assignments, number of required service hours at each location and required frequency.

Transportation optimization was then run to determine optimal routes for the FSRs to take when servicing the pipeline locations. This was a unique set-up in that the vehicles were actually people, and their capacity was number of hours per day that they could work. Transit, service, wait and break times were all accounted for as components of the route make-ups. Constraints around service time windows, DOT hours of service, capacity limits and route restrictions during hazardous winter weather conditions at certain times of year were all taken into consideration. The algorithm solves to minimize costs so the rate structure was set up on a per-mile basis and hourly charge to minimize distance and time. Step functions were incorporated into all rate fields in the model to support such things as banded mileage rates, first stop free and standard and overtime charges.

Results

The energy and logistics company was able to use this model to balance workloads for FSRs while considering the impact to facility, personnel and transportation costs. The model showed that, with only a small increase in overall cost, there is great opportunity for improvement in work balancing and route efficiencies. They could also visually show their FSRs how the workload was balanced using mapping and Gantt charts, and the model was built to be flexible and reusable so that the company could adjust the data or add new pipeline locations to consider.

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